1.Understanding the ggplot index

  • Unlike base graphics, ggplot works with dataframes and not individual vectors
  • All the data needed to make the plot is typically contained within the dataframe supplied to the ggplot() itself or can be supplied to respective geoms (layers).
  • The second noticeable feature is that you can keep enhancing the plot by adding more layers (and themes) to an existing plot created using the ggplot() function.
options(scipen = 999) # turn off scientific notation like 1e+06
library(ggplot2)
data("midwest", package = "ggplot2") # load the data

# Explore the data

midwest  %>%  dim
## [1] 437  28
midwest %>% head
## # A tibble: 6 x 28
##     PID    county state  area poptotal popdensity popwhite popblack
##   <int>     <chr> <chr> <dbl>    <int>      <dbl>    <int>    <int>
## 1   561     ADAMS    IL 0.052    66090  1270.9615    63917     1702
## 2   562 ALEXANDER    IL 0.014    10626   759.0000     7054     3496
## 3   563      BOND    IL 0.022    14991   681.4091    14477      429
## 4   564     BOONE    IL 0.017    30806  1812.1176    29344      127
## 5   565     BROWN    IL 0.018     5836   324.2222     5264      547
## 6   566    BUREAU    IL 0.050    35688   713.7600    35157       50
## # ... with 20 more variables: popamerindian <int>, popasian <int>,
## #   popother <int>, percwhite <dbl>, percblack <dbl>, percamerindan <dbl>,
## #   percasian <dbl>, percother <dbl>, popadults <int>, perchsd <dbl>,
## #   percollege <dbl>, percprof <dbl>, poppovertyknown <int>,
## #   percpovertyknown <dbl>, percbelowpoverty <dbl>,
## #   percchildbelowpovert <dbl>, percadultpoverty <dbl>,
## #   percelderlypoverty <dbl>, inmetro <int>, category <chr>

Initialising a ggplot

ggplot(data = midwest, mapping = aes(x = area, y = poptotal))

Notes:

  • Blank ggplot
  • no points or lines since nothing is specified - whether a scatterplot or a linechart

2.Simple Scatterplot

ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point()

Adding a smoothing layer using geom_smooth()

g <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) + 
        geom_point()+
        geom_smooth(method = "lm")
plot(g)

3.Adjusting the X and Y limits

Method - 1 : By deleting the points outside the range

This can be done using the xlim() and ylim() .

g <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point()+
        geom_smooth(method = "lm")

# Delete the points outside the limits

g + xlim(c(0,0.1)) + ylim(c(0,1000000)) # deletes points
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).

# Same thing 
g + xlim(0,0.1) + ylim(0,1000000) # deletes points
## Warning: Removed 5 rows containing non-finite values (stat_smooth).

## Warning: Removed 5 rows containing missing values (geom_point).

Method 2 : Zooming in

Change the X and Y axis limits by zooming in to the region of interest without deleting the points. This is done using coord_cartesian().

g <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point()+
        geom_smooth(method = "lm")
g1 <- g + coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000)) # zooms in 
plot(g1)

Here the line of best fit will not change since all the points were considered for plotting the line of best fit.

4.Change the Title and Axis Labels

Use the labs() function with title, x and y arguments. Also, we can use ggtitle(). xlab(). ylab()

g <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point() +
        geom_smooth(method = "lm") 
g1 <- g + coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000)) # zooms in 
# plot(g1)

# Add title and Labels
g1 + labs(title = "Area Vs Population", subtitle = "From midwest dataset", y = "Population", x = "Area", caption = "Midwest Demographics")

# or 

g1 + ggtitle(label = "Area Vs Population", subtitle = "From midwest dataset") + xlab(label = "Area") + ylab(label = "Population")

Here’s the full function call

ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point()+
        geom_smooth(method = "lm")+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title = "Area Vs Population", subtitle = "From midwest dataset", x = "Area", y = "Population", caption = "Midwest Demographics")

# or

ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) + 
        geom_point()+
        geom_smooth(method = "lm")+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        ggtitle(label = "Area Vs Population", subtitle = "From Midwest dataset ")+
        xlab(label = "Area")+
        ylab(label = "Population")+
        labs(caption = "Midwest Demographics")

5. Changing the color and size of points

We can change the aesthetics of a geom layer by modifying the respective geoms.

ggplot(data = midwest, mapping = aes(x = area, y = poptotal))+
        geom_point(col = "steelblue", size = 3)+
        geom_smooth(method = "lm", col = "firebrick")+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title = "Area Vs Population", subtitle = "From midwest dataset", y = "Population", x = "Area", caption = "Midwest Demographics")

Changing the color to reflect categories in another column

If we want to color to change based on anotehr column in the source dataset, it must be specified inside the aes() function

gg <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal))+
        geom_point(aes(col= state), size = 3)+
        geom_smooth(method = "lm", col = "firebrick", size = 2) +
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
plot(gg)

The legend is added automatically. If needed, it can be removed by setting the legend.position to None from within a theme() function.

gg+theme(legend.position = "None") # remove legend

Also, you can change the color pallette entirely

gg+scale_color_brewer(palette = "Set1") # change color pallette

More of such pallettes can be found in RcolorBrewer package

6. Changing the X-Axis texts and Ticks Location

Changing the X and Y Axis Text and its Location

This involves two aspects : breaks and labels

Step1 : Set the breaks

gg <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal))+
        geom_point(aes(col= state), size = 3)+
        geom_smooth(method = "lm", col = "firebrick", size = 2) +
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")

# Change breaks 
gg+scale_x_continuous(breaks = seq(0,0.1,0.01))

Step 2 : Change the labels

Change the labels at the axis ticks. labels take a vactor of the same length as breaks

gg <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point(aes(col = state), size =3)+
        geom_smooth(method = "lm", col = "firebrick", size =2)+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")

# Change breaks + label
gg+scale_x_continuous(breaks = seq(0,0.1,0.01), labels = letters[1:11])

If you need to reverse the scale, use scale_x_reverse().

gg <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point(aes(col = state), size =3)+
        geom_smooth(method = "lm", col = "firebrick", size =2)+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")

# Reverse X axis scale

gg + scale_x_reverse()

Writing customised texts for axis labels, by formatting the original values

Method 1: Using sprintf()

Method 2 : Using a custom defined function

gg <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point(aes(col = state), size =3)+
        geom_smooth(method = "lm", col = "firebrick", size =2)+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")

# Change Axis texts 

gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = sprintf("%1.2f%%", seq(0, 0.1, 0.01))) + 
        scale_y_continuous(breaks=seq(0, 1000000, 200000), labels = function(x){paste0(x/1000, 'K')})

Customizing the entire theme in one shot using pre-built themes

# Base plot 

gg <- ggplot(data = midwest, mapping = aes(x = area, y = poptotal)) +
        geom_point(aes(col = state), size =3)+
        geom_smooth(method = "lm", col = "firebrick", size =2)+
        coord_cartesian(xlim = c(0,0.1), ylim = c(0,1000000))+
        labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")


gg <- gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01))

# method 1 : Using theme_set()

theme_set(theme_classic()) # not run 
plot(gg)

# method 2 : adding theme layer itself 

gg+theme_bw() + labs(subtitle = "BW Theme")

gg+theme_classic()+labs(subtitle = "Classic theme")